Discovery of Probabilistic Mappings between Taxonomies: Principles and Experiments

Rémi Tournaire 1, 2 Petit Jean-Marc 1 Marie-Christine Rousset 2, * Alexandre Termier 2
* Corresponding author
1 BD - Base de Données
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 LIG Laboratoire d'Informatique de Grenoble - HADAS
LIG - Laboratoire d'Informatique de Grenoble
Abstract : In this paper, we investigate a principled approach for defining and discovering probabilistic mappings between two taxonomies. First, we compare two ways of modeling probabilistic mappings which are compatible with the logical constraints declared in each taxonomy. Then we describe a generate and test algorithm which minimizes the number of calls to the probability estimator for determining those mappings whose probability exceeds a certain threshold. Finally, we provide an experimental analysis of this approach.
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Rémi Tournaire, Petit Jean-Marc, Marie-Christine Rousset, Alexandre Termier. Discovery of Probabilistic Mappings between Taxonomies: Principles and Experiments. Journal on Data Semantics, Springer, 2011, 6720, pp.66-101. ⟨10.1007/978-3-642-22630-4_3⟩. ⟨hal-00932491⟩

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